Radio location finding

ABSTRACT

A method (1) for passively locating a radio emission source (2a, 2b) is described. The method includes including receiving radio signal datasets (D) corresponding to each of three of more sensors (3). Each sensor (3) includes at least one radio receiver (4). The method also includes receiving or retrieving a physical location corresponding to each sensor (3). The physical locations define a convex hull (5). The method also includes determining whether an emitter signal (8) within a target frequency range is present in any of the radio signal datasets (D), and assigning any radio signal dataset which comprises the emitter signal as a detection dataset. The method also includes, in response to determining three or more detection datasets, calculating a signal location (r) based on arrival times of the emitter signal and the respective physical locations. The method also includes generating a locus of possible positions based on calculating two or more alternative signal locations. Each alternative signal location is calculated by adding synthetic noise to one or more of the detection datasets and repeating the calculations used to calculate the signal location. When the signal location is inside the convex hull, cluster filtering based on circles or spheres is applied. When the signal location is outside the convex hull, cluster filtering is based on ellipses or ellipsoids and on the locus of possible positions. The method also includes outputting one or more estimated radio emission source locations. Each estimated radio emission source location is determined based on a respective cluster of signal locations.

FIELD OF THE INVENTION

The present invention relates to methods, apparatuses and systems forpassive location finding of radio emission sources using time-of-arrivalmethods.

BACKGROUND

Detection and location of unauthorised short-burst radio transmittingdevices is important for both security and safety applications.Time-of-arrival methods, based on differences in transit time between anactively emitting object and a number of sensors, represent one approachto passively detecting and tracking radio transmitting devices.

One example is the increasing availability and use of unmanned aerialvehicles, or drones, and the issues with drones being flown intorestricted airspaces. Detecting unauthorised drones may be important forsecurity/privacy, for example camera drones flown over sporting eventsfor which valuable television rights have been sold. In othersituations, drones may be flown (unintentionally or maliciously) over anairport, which can endanger aircraft and force runways to be shutdownleading to long delays.

Drones are often small and mainly made from low density materials suchas polymers. Consequently, drones are difficult to locate and/ordifferentiate from birds using traditional airspace control methods suchas radar. Drones may be located by monitoring the radio signals whichthey emit—for example to update an operator as to the drone location, totransmit still or video images and so forth. Conventionally, this isaccomplished by monitoring the narrow ranges of radio spectrum normallyused for controlling drones, often in combination with a library ofsignal patterns for known types of drone. However, such approaches havethe drawback that a drone must be known and characterised in advance inorder to be detected reliably.

SUMMARY

According to a first aspect of the invention there is provided a methodof passively locating a radio emission source, including receiving radiosignal datasets corresponding to each of three of more sensors. Eachsensor includes at least one radio receiver. The method also includesreceiving or retrieving a physical location corresponding to eachsensor. The physical locations define a convex hull. The method alsoincludes determining whether an emitter signal within a target frequencyrange is present in any of the radio signal datasets, and assigning anyradio signal dataset which comprises the emitter signal as a detectiondataset. The method also includes, in response to determining three ormore detection datasets, calculating a signal location based on arrivaltimes of the emitter signal and the respective physical locations. Themethod also includes generating a locus of possible positions based oncalculating two or more alternative signal locations. Each alternativesignal location is calculated by adding synthetic noise to one or moreof the detection datasets and repeating the calculations used tocalculate the signal location. The method also includes, in response tothe signal location is within the convex hull, applying a first clusterfilter to the signal location and previously calculated signal locationswithin a preceding time period. The first cluster filter appliescircular or spherical boundaries having a fixed radius for each of thesignal location and the previously calculated signal locations. Themethod also includes, in response to the signal location is outside theconvex hull, applying a second cluster filter to the signal location andthe previously calculated signal locations within the preceding timeperiod. The second cluster filter applies elliptical or ellipsoidalboundaries for each of the signal location and the previously calculatedsignal locations. Each elliptical or ellipsoidal boundary has a longaxis and a short axis with length equal to the fixed radius. A ratio ofthe long and short axes is equal to a ratio of maximum and minimumdistances spanning the respective locus of possible locations. The longaxis is aligned parallel to the maximum distance. The method alsoincludes outputting one or more estimated radio emission sourcelocations. Each estimated radio emission source location is determinedbased on a respective cluster of signal locations.

The method is computer implemented. Each sensor may include two or moreradio receivers. Each radio receiver may be a wide-band receiver. Awide-band receiver may correspond to a bandwidth of 20 MHz or more, 40MHz or more, or 100 MHz or more. Each radio receiver may be anarrow-band receiver. A narrow band receiver may correspond to abandwidth of 10 MHz or less, 5 MHz or less, or 1 MHz or less. Eachsensor may include a number of radio receivers, each tuned to adifferent frequency range.

The signal location may be calculated using the three detection datasetscorresponding to the strongest signals. The signal location may becalculated using the detection datasets which correspond to the longestbaseline. The signal location may be calculated using the detectiondatasets which correspond to the largest area. The area corresponding toa group of three or more detection datasets may correspond to a secondconvex hull defined by the respective physical locations of that groupof three or more detection datasets. The signal location may becalculated using all of the detection datasets.

The first and second cluster filters may include, or take the form of,nearest neighbour cluster analysis methods.

The one or more estimated radio emission source locations may be outputto a display, via a message sent over a network connection, via SMS, viae-mail, by causing a speaker to output an alarm signal, and so forth.

Determining whether the emitter signal within the target frequency rangeis present in any of radio signal datasets may include, or take the formof, performing a correlation analysis of each radio signal datasetagainst each other radio signal dataset.

The correlation analysis may include, or take the form of, asliding-window correlation analysis. The correlation may be calculatedbased on amplitude data. The correlation may be calculated based oncomplex IQ data.

The method may also include, in response to determining three detectiondatasets, calculating the signal location in two dimensions. When thesignal location is calculated in two dimensions, the first clusterfilter may apply circular boundaries and the second cluster filter mayapply elliptical boundaries.

The method may also include, in response to determining four or moredetection datasets, calculating the signal location in three dimensions.The signal location may be calculated using the four detection datasetscorresponding to the strongest signals. The signal location may becalculated using the four detection datasets which correspond to thelongest baseline. The signal location may be calculated using thedetection datasets which correspond to the largest area. The areacorresponding to a group of four or more detection datasets maycorrespond to a second convex hull defined by the respective physicallocations of that group of four or more detection datasets. The signallocation may be calculated using all of the detection datasets.

The signal location may only be calculated in response to determiningfour or more detection datasets, and the signal location may becalculated in three dimensions.

The signal location and/or any previously calculated signal locationsmay be projected onto a two-dimensional surface prior to application ofthe first and second cluster filters.

When the signal location and/or any previously calculated signallocations are projected onto a two-dimensional surface prior toapplication of the first and second cluster filters, the first clusterfilter may apply circular boundaries and the second cluster filter mayapply elliptical boundaries. The two-dimensional surface may take theform of a plane. The two-dimensional surface may take the form of aportion of a spherical surface. The two-dimensional surface maycorrespond to the ground, i.e. the surface of the earth, in the form ofland or water or a combination, depending on the physical locations ofthe three of more sensors.

Adding synthetic noise to a detection dataset may include applying atemporal offset to that detection dataset. The temporal offset may be afixed interval. To calculate an alternative signal location, at leastone of the detection datasets may have the fixed interval added orsubtracted, and each other detection dataset may be unchanged, may havethe fixed interval added, or may have the fixed interval subtracted.

Generating the locus of possible positions may include, for eachdetection dataset in turn, shifting that detection dataset forwards bythe fixed interval and calculating an alternative signal location, thenshifting that detection dataset backwards by the fixed interval andcalculating another alternative signal location, such that the totalnumber of alternative signal locations calculated will be equal to twicethe number of detection datasets.

Generating the locus of possible positions may include calculating analternative signal location corresponding to every possible permutation(excluding doing nothing at all) of the detection datasets with a set ofoperations including adding the fixed interval, subtracting the fixedinterval and doing nothing. When every possible permutation iscalculated, the number of alternative signal locations will be threetimes the number of detection datasets, minus one (doing nothing for alldetection datasets simply corresponds to the signal location).

Applying a temporal offset to one or more of the detection datasets mayinclude generating, using a probability density function centred at zerooffset, a different temporal offset for each of the detection datasetsused to calculate the signal location. Calculating two or more alternatesignal locations may include calculating a number of alternate signallocations based on generating different pseudo-random temporal offsetseach time. The number of alternate signal locations generated may be 10or more, 20 or more, 50 or more, or 100 or more.

Adding synthetic noise to a detection dataset may include generating anoise signal and adding it to that detection dataset. The noise signalmay be generated according to probability density function centred atzero. The detection dataset may include a time series of signal valuesto which the noise signal may be added. The detection dataset mayinclude in-phase and quadrature (IQ) values. Adding synthetic noise mayinclude adding the same noise signal to I and Q components. Addingsynthetic noise may include adding different noise signals to the I andQ components.

Calculating two or more alternate signal locations may includecalculating a plurality of alternate signal locations, each timegenerating different pseudo-random noise values for the detectiondatasets. The total number of alternate signal locations generated maybe 10 or more, 20 or more, 50 or more, or 100 or more.

The locus of possible positions may be generated by fitting a curve orsurface to the alternative locations.

The curve or surface may be fitted so as to minimise its respective areaor volume subject to the boundary enclosing the signal location and athreshold fraction of the alternative locations. The curve or surfacemay additionally be constrained to a particular shape. For example, thecurve or surface may take the form of an elliptical or spheroidal(prolate or oblate) boundary, with degrees of freedom for the fittingcorresponding to the orientation of the axes and the lengths of eachaxis. Alternatively, instead of fitting a particular shape, the locus ofpossible locations may be generated by fitting a piecewise continuoussurface. The locus of possible positions may correspond to a confidenceof 50% or more, 60% or more, 70% or more, 75% or more, 80% or more, 90%or more, or 95% or more. The locus of possible locations may enclose athreshold fraction of 0.5, 0.6. 0.7. 0.75, 0.8, 0.85, 0.9, 0.95, 0.98 or0.99 of the alternative signal locations.

An estimated radio emission source location may be determined and outputto correspond to each cluster of signal locations which includes morethan a threshold number.

Each estimated radio emission source location may be determined as anaverage of a respective cluster of signal locations. The average ispreferably a mean or a weighted mean. For example, each signal locationin a cluster may be weighted inversely to an area or volume of therespective locus of possible locations. The average may be a median.

Each estimated radio emission source location may be determined byfitting a linear regression line to the respective cluster of signallocations, and extrapolating the linear regression line to an outputtime.

Additionally or alternatively, every signal location belonging to acluster of signal locations may be output as a separate estimated radioemission source location.

The method may also include tracking clusters of signal locations acrossthe preceding time period, such that in response to a cluster isstationary, the estimated radio emission source location is determinedas an average of the signal locations belonging to that cluster, and inresponse to a cluster is moving, a linear regression line is fitted tothe signal locations belonging to that cluster and the linear regressionline is extrapolated to an output time.

Each new cluster may be initialised as stationary (a new cluster may beone having no corresponding cluster during an immediately previousiteration of the method). A stationary cluster may be changed to amoving cluster in response to a speed of the respective estimated radioemission source location exceeding a motion threshold. A moving clustermay be changed to a stationary cluster in response to a speed of therespective estimated radio emission source location being below a staticthreshold. The motion threshold may be equal to the static threshold,but does not need to be equal. Determination of whether a cluster ismoving or stationary may be based at least on part on Doppler frequencyshifts between the radio signal datasets.

The method may also include causing one or more optical telescopesand/or hardware drone countermeasures to be directed towards acorresponding estimated radio emission source location. Dronecountermeasures may include one or more lasers, radio frequency jammers,global positioning system (GPS) spoofers, high power microwave devices,net launchers, interception drones and so forth.

The method may also include, based on the signal location and thepreviously calculated signal locations within the preceding time period,determining a bearing angle which maximises a number of signal locationswithin an angular threshold of the bearing angle.

The bearing angle may be calculated to originate from a centroid of thesensors. The bearing angle may originate from a user defined locationwithin the convex hull. The bearing angle may originate from a physicallocation corresponding to one of the sensors, an optical telescope or ahardware drone countermeasure.

The bearing angle may be output. Output of the bearing angle may beconditional on the number of signal locations within an angularthreshold of the bearing angle exceeding a threshold number. When thebearing angle is calculated to originate from a physical location of anoptical telescope or a hardware drone countermeasure, the method mayinclude orienting that telescope or hardware drone countermeasure topoint along the bearing angle.

Determining the bearing angle may include calculating an angularhistogram having a bin width equal to the angular threshold. The bearingangle may correspond to a central angle of an angular histogram bincontaining the greatest number of signal locations. Alternatively, thebearing angle may be swept through an arc in angular increments until apeak is found. The angular increments may be smaller than the angularthreshold.

The method may also include receiving or calculating an outer perimetersuch that, when viewed from any position on the outer perimeter, theconvex hull subtends a fixed angle. Application of the first clusterfilter may be further conditional upon the signal location is within theouter perimeter. Application of the second cluster filter may be furtherconditional upon the signal location is within the outer perimeter.

The fixed angle for definition of the outer perimeter may be an anglebetween 20 and 40 degrees, more preferably 25 to 35 degrees, and mostpreferably between 29 and 31 degrees.

In this way, the bearing angle may always be calculated to provide atleast a direction to the radio emission source. As the radio emissionsource moves closer and crosses the outer perimeter, cluster filteringto calculate and output an estimated radio emission source location maybe carried out using the second cluster filter, in addition to thebearing angle. As the radio emission source moves closer still andcrosses inside the convex hull, the first cluster filter may be appliedto determining an estimated radio emission source location. Calculationof the bearing angle may be stopped when the radio emission sourcecrosses inside the convex hull. Calculation of the bearing angle may becontinued when the radio emission source crosses inside the convex hull.

According to a second aspect of the invention, there is provided amethod of calculating a bearing to a radio emission source, includingreceiving radio signal datasets corresponding to each of three of moresensors. Each sensor includes at least one radio receiver. The methodalso includes receiving or retrieving a physical location correspondingto each sensor. The physical locations define a convex hull. The methodalso includes determining whether an emitter signal within a targetfrequency range is present in any of the radio signal datasets, andassigning any radio signal dataset which comprises the emitter signal asa detection dataset. The method also includes, in response todetermining three or more detection datasets, calculating a signallocation based on arrival times of the emitter signal and the respectivephysical locations. The method also includes, based on the signallocation and previously calculated signal locations within a precedingtime period, determining a bearing angle which maximises a number ofsignal locations within an angular threshold of the bearing angle. Themethod also includes outputting the bearing angle.

The method of the second aspect may include features corresponding toany features of the method of the first aspect. Definitions applicableto the method of the first aspect may be equally applicable to themethod of the second aspect.

According to a third aspect of the invention, there is providedapparatus for passively locating a radio emission source, including acommunications interface configured to receive radio signal datasetscorresponding to each of three of more sensors. Each sensor includes atleast one radio receiver. The communications interface is furtherconfigured to receive a physical location corresponding to each sensor,or the apparatus stores the physical locations and is configured toretrieve the physical locations. The physical locations define a convexhull. The apparatus is configured to carry out a method according to thefirst or second aspects of the invention.

The apparatus may include features corresponding to any feature of themethod of the first aspect or the method of the second aspect.Definitions applicable to the method of the first aspect or the methodof the second aspect may be equally applicable to the apparatus.

A system may include three or more sensors. Each sensor may include atleast one radio receiver. Physical locations of the sensors may define aconvex hull. The system also includes the apparatus, and the apparatusis configured to receive respective radio signal datasets from the threeor more sensors.

The apparatus may be configured to receive radio signal datasets viawired or wireless links. The apparatus may be configured to receiveradio signal datasets via a mixture of wired or wireless links betweenthe sensors and the apparatus. For example, some sensors may beconnected to the apparatus by wired links, whilst other sensors may beconnected by wireless links.

Each sensor may be configured to transmit the corresponding radio signaldataset continuously. In other words, each sensor may transmits itsradio signal dataset live or in “real time” with monitoring the spectrumor spectra corresponding to that sensor's radio receiver(s), or with aslittle delay as possible.

Each sensor may be configured to locally cache the respective radiosignal dataset and to transmit the cached radio signal dataset to theapparatus in batches. Each sensor may transmit the cached radio signaldataset to the apparatus according to a predetermined schedule.Alternatively, each sensor may transmit the cached radio signal datasetto the apparatus in response to receiving a signal from the apparatus.Alternatively, each sensor may be configured to locally cache therespective radio signal dataset without transmitting it. In this case,the sensors may be intended for collection and transport to a centrallocation for retrieval of the cached radio signal dataset.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain embodiments of the present invention will now be described, byway of example, with reference to the accompanying drawings, in which:

FIG. 1 is a schematic plan view of a system for radio location finding;

FIG. 2 is a schematic block diagram of the system shown in FIG. 1 ;

FIG. 3 is a process flow diagram of a method of radio location finding;

FIG. 4 is a schematic block diagram of a sensor for use in the systemshown in FIG. 1 ;

FIG. 5 schematically illustrates the same signal being received atdifferent times by sensors at different physical locations;

FIGS. 6A to 6D schematically illustrate a sliding window correlationanalysis between a pair of signals;

FIG. 7 schematically illustrates applying synthetic noise to a set ofcorrelated signals to estimate the sensitivity of a calculated signallocation to noise;

FIG. 8 schematically illustrates determining a locus of possiblepositions representing the sensitivity of a calculated signal locationto noise;

FIG. 9 schematically illustrates variability in sequentially calculatedsignal locations;

FIG. 10 schematically illustrates application of a first cluster filter;

FIG. 11 schematically illustrates application of a second clusterfilter;

FIG. 12 schematically illustrates angular bins for determining a bearingangle;

FIG. 13 is a histogram corresponding to FIG. 12 ;

FIG. 14 schematically illustrates the definition of an outer perimeterfor an arrangement of sensors; and

FIG. 15 schematically illustrates the definition of an outer perimeterfor a different arrangement of sensors to that shown in FIG. 14 .

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

In the following, like parts are denoted by like reference numerals.

The present specification concerns improvements in time-of-arrival basedmethods for determining a location of and/or bearing to a radio emissionsource. In particular, the present specification concerns improvementsin estimating radio emission source locations which are outside a convexhull defined by the locations of sensors (radio receivers).

The methods described herein may be applied to wide-band radioreceivers, removing the need to know a transmission frequency of a radioemission source in advance.

Although often described herein in relation to detection of unauthorisedradio emission sources such as drones, the methods of the presentspecification are equally applicable to any application concerningpassive location of one or more radio emission sources.

Referring to FIG. 1 , a system 1 for passively locating a radio emissionsource 2 is shown.

Referring also to FIG. 2 , a block diagram of the system 1 is shown.

The system 1 includes three or more sensors 3. Each sensor 3 includes(or takes the form of) at least one radio receiver 4 (FIG. 4 ). Thesensors 3 are spread across a region, for example around and/or withinthe perimeter of an airport. The physical locations of each sensor 3,for example coordinates (x, y) in a local coordinate system or alatitude and longitude, are either known in advance or included in datasent from that sensor 3. The physical locations of the sensors 3 definea convex hull 5. As explained herein, the processing to estimate alocation of a radio emission source 2 differs between radio emissionsources 2 a estimated to lie within the convex hull 5 and radio emissionsources 2 b estimated to lie outside the convex hull 5. There may be anynumber N of sensors 3 in the system 1, although the minimum number isthree, i.e. N≥3.

The system 1 includes an apparatus 6 for passively locating one or moreradio emission sources 2. The apparatus includes a communicationsinterface (not shown) configured to receive radio signal datasets D(FIG. 4 ) corresponding to each of the sensors 3 via wired and/orwireless links 7. In some examples, the apparatus 6 may receive radiosignal datasets D from the sensors 3 via a mixture of wired or wirelesslinks 7. For example, some sensors 3 may be connected to the apparatus 6by wired links 7, whilst other sensors 3 are connected to the apparatus6 by wireless links 7.

If the physical locations of one or more of the sensors 3 are not storedin advance within the apparatus 6, the communications interface (notshown) of the apparatus 6 may be further configured to receive aphysical location corresponding to each sensor 3. In someimplementations, one or more of the sensors 3 may be mobile/moveable,and may include location sensors such as, for example, an inertialcompass, a global position system (GPS) receiver and so forth. Suchmobile sensors 3 may update the apparatus 6 of their present locationperiodically, continuously, in response to a request from the apparatus6, by including the current physical location in the corresponding radiosignal dataset D, and so forth.

The system 1 is a passive location-finding system, in the sense that itrelies upon detecting radio frequency signals 8 emitted by a radioemission source 2 in order to estimate a location for that radioemission source 2. This is in contrast to an active location-findingsystem such as radar.

In some implementations, each sensor 3 may transmit a correspondingradio signal dataset D continuously. In other words, each sensor maytransmit its radio signal dataset D live or in “real time” withmonitoring the spectrum or spectra corresponding to that sensor's radioreceiver(s) 4 (FIG. 4 ). In practice, the terms “live” and/or “realtime” mean transmitting with as little delay as possible, i.e. as soonas the radio signal dataset is available with no imposed delay. Such aconfiguration may be applicable to a use in which there is no concernabout the sensors 3 being located by another party, for example, whendetecting unauthorised drones being flown into/near restricted airspace.

In other implementations, each sensor 3 may be configured to locallycache its respective radio signal dataset D, and to transmit the cachedradio signal dataset to the apparatus 6 in batches. For example,according to a predetermined schedule, or in response to receiving asignal from the apparatus 6. Radio signal datasets D may be transmittedregularly, for example several times per second, or every few seconds orminutes. Alternatively, radio signal datasets D may be transmittedsporadically, for example every ten minutes, every hour, or at longerintervals.

Longer caching periods may be particularly useful when live informationis not important. For example, a municipal authority may wish todetermine how many drones are being flown, for how long, and over whichlocations, as part of routine planning. A number of battery poweredsensors 3 may be distributed around a region to record radio signaldatasets. The radio signal datasets may be retrieved by an operatorvisiting the sensors 3 with the apparatus 6 or a suitable portabledevice, and connecting to the sensor 3 using a wired cable, a shortrange wireless communication protocol such as Bluetooth® and so forth.

Another approach which uses locally cached radio signal datasets is todeploy battery powered, portable sensors 3 around an area for aduration, followed by retrieving the sensors 3 and returning to acentral location for data retrieval.

Battery powered sensors 3 may be used in any example of the system 1 forwhich a mains connection may be difficult or impractical, whethertransmission of radio signal datasets D is continuous, periodic or ondemand. Battery powered sensors 3 may be rechargeable using energyharvesting devices such as solar panels, wind turbines etc.

Referring also to FIG. 3 , a process flow diagram of a method ofpassively locating a radio emission source 2 is shown.

The method is computer implemented, for example executed by one or moredigital electronic processors of the apparatus 6. The apparatus 6 maytake the form of a dedicated device, for example an application specificintegrated circuit (ASIC). Alternatively, the apparatus 2 may take theform of a suitable programmed microcontroller, general purpose computer,and so forth.

Radio frequency datasets D corresponding to each of N≥3 sensors 3 arereceived (step S1). For convenience of reference in the followingdiscussions, let the radio signal dataset D received from the n^(th) ofN sensors 3 be denoted D_(n). The radio signal datasets D₁, . . . ,D_(n), . . . , D_(N) may take any suitable format, for example, in-phaseand quadrature, I(t), Q(t) radio signal data corresponding to each radioreceiver 4 (FIG. 4 ) of the respective sensor 3. Additionally oralternatively, radio signal datasets D_(n) may include, or take the formof, more processed data such as power spectra corresponding to eachradio receiver 4 (FIG. 4 ) of the respective sensor 3. In some examples,a radio signal dataset D_(n) received in the form of raw IQ data may beaugmented during this step by the apparatus 6 calculating thecorresponding power spectra.

Referring also to FIG. 4 , a block diagram of an exemplary sensor 3 isshown.

The sensor 3 includes at least one antenna 9, one or more radioreceivers 4 and a controller 10 for managing and/or implementing thefunctions of the sensor 3. The antenna 9 may be a single antenna, or anarray of antennae such as a phased array. The antenna 9 (or antennae 9)may take the form of one or more omnidirectional antennae, one or moredirectional antennae, or a mixture of omnidirectional antennae anddirectional antennae.

Whilst only a single radio receiver 4 ₁ is required, in general thesensor 3 may include any number M≥1 of radio receivers 4 ₁, 4 ₂, . . . ,4 _(m), . . . , 4 _(M). Each radio receiver 4 _(m) may include, or beconnected to the antenna(e) 9 via, a respective passband filter 11 ₁, .. . , 11 _(M). In this way, each radio receiver 4 _(m) may receive adifferent frequency range from each other receiver 4 _(k≠m). Thefrequency ranges of passband filters 11 _(m) may partially overlap withthe frequency range(s) of one or more other passband filters 11 _(k≠m).Alternatively, in some implementations the frequency ranges of thepassband filters 11 ₁, . . . , 11 _(M) may be mutually exclusive.

Radio frequency signals 8 received by the antenna(e) 9 are filtered bythe one or more passband filters 11 ₁, . . . , 11 _(M) and correspondingpassband signals 12 ₁, . . . , 12 _(M) are passed to the respectiveradio receivers 4 ₁, . . . , 4 _(M). Each radio receiver 4 _(m) measuresIQ data 13 _(m) and passes this to the controller 10 of the sensor 3.The controller 10 compiles the radio signal dataset D for the sensor 3,and transmits it (wired or wirelessly) to the apparatus 6.

In general, each radio receiver 4 (or the combination of radio receiver4 and passband filter 11) may take the form of a wide-band receiver, forexample having a bandwidth of 20 MHz or more, 40 MHz or more, or 100 MHzor more. Wide-band receivers may be particularly useful when thefrequencies of signals 8 from radio emissions sources 2 are not known inadvance. In other implementations, for example when the bandwidth ofsignals 8 from radio emissions sources 2 is known or predictable inadvance, some or all of the radio receivers 4 may take the form ofnarrow-band receivers, for example each corresponding to bandwidths of10 MHz or less, 5 MHz or less, or 1 MHz or less.

The sensor 3 shown in FIG. 4 is only one example of a suitable sensor 3configuration. In general the system 1 may use any sensor 3 capable ofdetecting and recording radio signals 8.

Referring again to FIG. 3 in particular, the physical locationscorresponding to each sensor 3 are received or retrieved (step S2). Forexample, each of N≥3 sensors 3 may correspond to a location expressed ina local coordinate system (x, y), or in terms of latitude and longitude.The physical locations of sensors 3 may also include an altitude/heightof the sensor.

The method includes determining whether an emitter signal 8 within atarget frequency range f_(low)≤f≤f_(high) is present in any of the radiosignal datasets D₁, . . . , D_(N), and any radio signal dataset D_(n)which includes the emitter signal 8 is assigned as a detection datasetDET (step S3). The determination may be made by performing a correlationanalysis amongst the radio signal datasets D₁, . . . , D_(N). Thecorrelation analysis may be useful because detecting a correlationbetween a pair of radio signal datasets D_(n), D_(k≠n) may allowdetermining the presence of an emitter signal 8 at a lowersignal-to-noise ratio than would be required if, for example, anamplitude threshold was used to trigger the process. In otherapplications, where signal strength is of less concern, it may bepossible to use a detection based on exceeding a threshold to trigger acorrelation analysis. In general, a number 0≤J≤N of the radio signaldatasets D₁, . . . , D_(N) may include an emitter signal 8 within thetarget frequency range {f_(low); f_(high)}. Hereinafter let the j^(th)of J detection datasets be denoted as DET_(j).

The target frequency range {f_(low); f_(high)} may be set and/oradjusted based on user input and/or automatic analysis of power spectracorresponding to radio signal datasets D_(n). For example, the methodmay include displaying power spectra corresponding to one or more of theradio signal datasets D₁, . . . , D_(N) to a user via a graphical userinterface (GUI). In some implementations a power spectrum displayed to auser may be summed or otherwise aggregated across all of the radiosignal datasets D₁, . . . , D_(N). The GUI may allow a user to browsethe power spectrum (or spectra) in frequency and/or time, for example byallowing a user to define a frequency range and/or time period todisplay. The user may then set the target frequency range {f_(low);f_(high)} in response to spotting a signal within that frequency range.In some implementations, two or more different target frequency ranges{f_(low); f_(high)} may be set to permit independently estimatinglocations for two or more radio emission sources 2 operating indifferent frequency bands.

Additionally or alternatively, the target frequency range(s) {f_(low);f_(high)} may be set automatically based on analysis of received orcalculated power spectra corresponding to the radio signal datasets D₁,. . . , D_(N) (as explained herein, power spectra may be part of eachradio signal dataset D_(n) or may be calculated by the apparatus 6). Forexample,

the target frequency range {f_(low); f_(high)} may be set based ondetermining a peak in the power spectra and setting the target frequencyrange {f_(low); f_(high)} with a pre-set bandwidth and centred on thatpeak. Automated determination may be restricted to particular frequencybands and/or may exclude certain frequency bands. For example, if theapparatus 6 is to be used for detecting drones approaching and/orencroaching on the airspace of an airport, then automated setting of thetarget frequency range {f_(low); f_(high)} may be restricted tofrequency bands typically used by commercially available drones, and/orthe frequency bands used by the airport infrastructure may be excludedfrom automated determination of the target frequency range {f_(low);f_(high)}.

In some implementations, an initial target frequency range {f_(low);f_(high)} may be automatically determined and presented to a user viathe GUI, and the user may then either accept or adjust the targetfrequency range {f_(low); f_(high)} in terms of central frequency,bandwidth width and so forth.

Referring also to FIGS. 5 to 6D, the determination of which radio signaldatasets D_(n) are detection datasets DET_(j) is further discussed.

FIG. 5 shows signals (for example I or Q data) as a function of time fora number N=4 of sensors 3, with the amplitudes offset relative to oneanother on the amplitude axis for visual clarity. The signals shown inFIG. 5 are for illustrative purposes only, and are synthetic (notmeasured signal data).

In this illustrative example, all four sensors 3 have captured the samesignal 8, although the specific detected signal pulses 14 ₁, 14 ₂, 14 ₃,14 ₄ differ in that they include noise. Moreover, the signal 8 reacheseach sensor 3 at a slightly different time due to varying distancesbetween the radio emission source 2 and each sensor 3.

The radio signal 8 is detected by comparing the radio signal datasetsD_(n) against each other, for example using correlation analysis asexplained hereinafter. If a strong correlation is found between any pairof signals, 14 ₁, . . . , 14 ₄, then a signal has been detected and thecorresponding signals are assigned as detection datasets DET. A radiosignal 8 detected in this way from correlation analysis may then becross-correlated with all the remaining signals to determine whether anyothers beyond the initial pair captured the same radio signal 8.

Alternatively, the radio signal 8 may be detected when a pulse in one ofthe radio signal datasets D_(n) exceeds a threshold, for example fivetimes a calibrated standard error for the corresponding sensor 3. Thisoption may be useful for strong signals by allowing the length ofsignals which require correlation to be reduced. However, applying adetection threshold in this way may reduce the sensitivity of detection,because correlation analysis may detect a radio signal 8 has beencaptured in a pair of radio signal datasets D_(n), D_(k≠n) at a lowersignal-to-noise ratio, compared to applying an amplitude threshold.

In order to determine whether the same emitter signal 8 within thetarget frequency range {f_(low); f_(high)} is commonly detected in twoor more radio signal datasets D₁, . . . , D_(N), a correlation analysisis performed to compare each radio signal dataset D_(n) against eachother radio signal dataset D_(k≠n). Generally, the entire duration ofeach radio signal dataset D_(n) may be used, since it is not necessarilyknown in advance which if any of the radio signal datasets Dn may havecaptured a radio signal 8, or at what time. Alternatively, inapplications were a threshold amplitude is used to obtain a positivedetection, a portion of each radio signal dataset D_(n) corresponding toa shorter window of time may be used. The first detected signal 8,whether found from correlation or from a threshold detection, is notnecessarily the closest to the radio emission source 2 (for example afurther sensor 3 may have a clearer line-of-sight), and consequently theapparatus 6 may also need to retrieve (also referred to as“backhauling”) data from each other radio signal dataset D_(n) for atleast a period of time extending before and after the first detection.

The correlation analysis preferably takes the form of a sliding-windowcorrelation analysis, although any other technique known for signalcorrelations may be used instead. The correlation analysis may beperformed based on amplitude data, complex IQ data, and so forth.

For example, a correlation analysis between a pair of radio signaldatasets D_(n), D_(k≠n) may be calculated as:

CORR_(n,k)(Δt)=∫_(−∞) ^(∞) A _(n)(τ+Δt)A _(k)(τ)dτ  (1)

In which A_(n)(t) is the amplitude of a signal belonging to the n^(th)radio signal dataset D_(n), A_(k)(t) is the amplitude of thecorresponding signal belonging to the k^(th) radio signal dataset (k≠n),Δt is the offset in time applied to the signal A_(n)(t), and τ is adummy variable for the integration. The signals A_(n)(t) and A_(k)(t)may be normalised prior to calculating the value of the correlationintegral CORR_(n,k)(Δt). The signals A_(n)(t) and A_(k)(t) have finiteduration and will be evaluated using numerical integration of Equation(1). An identical length segment is selected from each signal A_(n)(t)and A_(k)(t). Either or both signals A_(n)(t) and A_(k)(t) may betruncated or padded using zero-insertion prior to numerical evaluationof Equation (1).

Referring in particular to FIG. 6A, the signals 14 ₃ (dashed line) and14 ₄ (solid line) shown in FIG. 5 are shown on the same axes withoutoffsetting along the amplitude axis and for a time offset of zero(Δt=0).

Referring in particular to FIGS. 6B and 6C, the same signal signals 14 ₃(dashed line) and 14 ₄ (solid line) are shown when the signal 14 ₃ forthe third sensor 3 are offset by different amounts towardscorrespondence with the signal 14 ₄ for the fourth sensor 3. It may beobserved that the contribution towards a net value of the correlationintegral CORR_(n,m)(Δt) will increase when the signals 14 ₃, 14 ₄ areoffset so as to correspond with one another.

Referring in particular to FIG. 6D, the correlation integralCORR_(n,k)(Δt) is evaluated for a range of time offsets Δt correspondingto the width of the sliding window. A peak in the value of thecorrelation integral CORR_(n,k)(Δt) represents a likely correlation. Aminimum threshold value of the correlation integral CORR_(n,k)(Δt) maybe applied to prevent false positives. In some examples, the correlationintegral CORR_(n,k)(Δt) may exhibit multiple peaks, in which case thelargest peak is taken to represent the best offset Δt value. A signalmay be detected as correlated to a first detected signal even if thatsignal has relatively worse signal-to-noise ratio.

In addition to determining whether two or more radio signal datasets D₁,. . . , D_(N) include detection of the same signal 8 and may be assignedas detection datasets DET_(j), the correlation analysis also determinesthe offsets Δt between arrival times of that signal 8 at each sensor 3.These offsets Δt are used later in the method for estimating thelocation of the radio emission source 2.

Referring again in particular to FIG. 3 if fewer than three detectiondatasets DET_(j) are determined, i.e. J<3 (step S4|No), then no estimateof radio emission source 2 location is made, and the method proceeds toreceiving subsequent radio signal datasets D₁, . . . , D_(N) (step S1).

However, if three or more detection datasets DET_(j) are determined,i.e. J≥3 (step S4|Yes), then the method proceeds to calculation of asignal location r for the radio emission source 2 based on arrival timesof the emitter signal 8 and the respective physical locations of thedetecting sensors 3 (step S5). For example, the previously calculatedoffsets Δt may be used. Estimating the signal location r of an emissionsource 2 based on time-of-arrival data may be carried out using anyknown method.

The signal location r calculated at this stage should be differentiatedfrom an estimated radio emission source location R which is subsequentlycalculated (step S10) based on calculated signal locations r spanning atime period T.

If there are only J=3 detection datasets DET_(j), then there is nochoice and all three must be used to calculated a signal location r intwo dimensions 9 (i.e. in a local coordinate system x-y orlatitude-longitude). However, if there are J≥4 detection datasetsDET_(j), then the signal location r may be estimated in three dimensionsto add an estimation of altitude/height. Whether the signal location ris calculated in two or three dimensions (using three or four detectiondatasets DET_(j)), if there are more detection datasets DET_(j) then theminimum required, then a subset of the J detection datasets DET_(j) maybe used to estimate the signal location r. A subset may be chosen basedon any suitable criteria including, but not limited to:

Using the subset (three or four) of detection datasets DET_(j)corresponding to the strongest signals;

Using the subset of detection datasets DET_(j) which correspond to thelongest baseline;

Using the subset of detection datasets DET_(j) which correspond to thelargest area. The area corresponding to a group of three of moredetection datasets DET_(j) may correspond to a second convex hulldefined by the respective physical locations of that group of detectiondatasets DET_(j);

Using a weighted combination of the preceding factors to determine thesubset to use.

Alternatively, all of the detection dataset DET₁, . . . , DET_(J) may beused to produce a refined estimate of the signal location rcorresponding to an emission source 2.

The calculation of the signal location r in two or three dimensions maybe made conditional upon the number J of detection datasets DET_(j), forexample in two dimensions when J=3, and in three dimensions when J≥4.Alternatively, the signal location r may always be calculated in two orthree dimensions (three dimensions would require the minimum value of Jto be set to J=4)

A locus of possible positions 15 (FIG. 8 ) is generated based oncalculating two or more alternative signal locations p (step S6). Eachalternative signal location p is calculated by adding synthetic noise toone or more of the detection datasets DET_(j), followed by repeating thesignal location calculations using the modified data.

Referring also to FIGS. 7 and 8 , generation of the locus of possiblepositions 15 is discussed.

Referring in particular to FIG. 7 , normalised correlation integralvalues are illustrated for a case in which there are J=5 detectiondatasets DET_(j). The correlation integral values shown in FIG. 7 aresynthetic for illustrative purposes.

One detection dataset DET_(j), for the sake of illustration the fifthDET₅, is taken as a reference, and the time offsets Δt are calculatedrelative to this reference. Offset Δt₁ is the time delay between thefirst DET₁ and fifth DET₅ detection datasets, offset Δt₂ is the timedelay between the second DET₂ and fifth DET₅ detection datasets, and soforth.

In one example, adding synthetic noise to a detection dataset DET_(j)may take the form of applying a temporal offset δt to a time differenceΔt corresponding to that detection dataset DET_(j). For example, tocalculate an alternative signal location p, at least one of thedetection datasets DET_(j) may have an offset δt added or subtracted,whilst each other detection dataset DET_(k≠j) may be unchanged, may havethe offset δt added, or may have the offset δt subtracted. The processof calculating the signal location r is then repeated using the modifiedtime differences Δt to calculate an alternative position location p. Forthe example shown in FIG. 7 , the modified time differences wouldcorrespond to {Δt₁±δt, Δt₂±δt, Δt₃±δt, Δt₄±δt}. Each temporal offset δtmay take the form of the same fixed time interval. Alternatively, insome examples each temporal offset δt may take the form of a (pseudo)randomly interval generated using a probability density function (i.e.every temporal offset δt value used is generated as a new, differentvalue).

When the temporal offset δt takes the form of a fixed interval, oneoption for generating the alternative signal locations p is to take eachdetection dataset DET_(j) in turn, then shift the time difference Δt forthat detection dataset DET_(j) forwards by the fixed temporal offset δtand calculate a first alternative signal location p, followed byshifting the time difference Δt for that detection dataset DET_(j)backwards by the fixed temporal offset δt and calculating a secondalternative signal location p. Once repeated for each detection datasetDET_(j), this will generate a number of 2.J alternative signal locationsp.

Alternatively, the locus of possible locations 15 may be generated bycalculating an alternative signal location p corresponding to everypossible permutation (excluding doing nothing at all) of adding thefixed interval δt, subtracting the fixed interval δt, and doing nothing.When every possible permutation is calculated, the number of alternativesignal locations p will be 3.J-1 (the minus one corresponds to the factthat doing nothing to the time differences Δt for all detection datasetssimply corresponds to the signal location r).

In a further implementation, each alterative signal location p may begenerated by applying a different pseudo-random temporal offset δt toeach time difference Δt, with the temporal offsets δt generated inaccordance with a probability density function centred at zero offset. Aset of alternative signal locations p may be built up by repeating thisprocess a predetermined number of times, for example 10 or more, 20 ormore, 50 or more, or 100 or more times.

An advantage of the preceding methods is that the temporal offset(s) δtare applied directly to the time differences Δt already known from thecorrelation analysis of step S3. In this way, a measure of thesensitivity of the signal location r calculation to noise may beobtained without the need to repeat the correlation analysis in full.Such approaches may be less computationally intensive, reducing latencyand/or power consumption.

In other examples, temporal offsets δt may be generated in any waydescribed hereinbefore, then instead applied to the raw data of thedetection datasets DET_(j), for example offsetting amplitude data and/orIQ data, followed by repetition of the correlation analysis.

Alternatively, adding synthetic noise to a detection dataset DET_(j) maytake the form of generating a noise signal (using a probability densityfunction) and adding the generated noise signal to that detectiondataset DET_(j), for example to the amplitude data and/or IQ data. Thecorrelation analysis may then be repeated to determine time differencesΔt for the modified datasets, and a corresponding alternative signallocation calculated. This process may be repeated a number of times tobuild up a set of alternative signal locations p, for example 10 ormore, 20 or more, 50 or more, or 100 or more times.

Once a set of at least two alternative signal locations p has beengenerated, the locus of possible locations 15 may be determined usingthe alternative signal locations p, and optionally also the calculatedsignal location r.

In one example, the locus of possible locations 15 may simply beassigned as the set of all the alternative signal locations p generated,optionally plus the calculated signal location r.

Another approach is to generate the locus of possible locations byfitting a curve (in two dimensions) or a surface (in three dimensions)to the alternative signal locations p (optionally plus the calculatedsignal location r).

For example, the locus of possible locations 15 may take the form of acurve (or surface) positioned, sized and orientated to enclose everyalternative signal location p (and optionally the calculated signallocation r) whilst minimising the area (or volume) within that curve (orsurface). Referring in particular to FIG. 8 , the rectangle 15 a (dashedline) having side lengths w₁ and w₂ may be assigned as the locus ofpossible locations 15. The rectangle 15 a may be fitted by varying thecentroid, angle, and lengths w₁, w₂, to find the minimum area w₁, w₂which encloses all of the alternative signal locations p.

A rectangle need not be used, and any other regular or irregular shapemay be used instead. Alternatively, instead of fitting a particularshape, the locus of possible locations 15 may be generated by fitting apiecewise continuous curve or surface, or a further convex hulldetermined based on the alternative signal locations p.

Instead of enclosing all of the alternative signal locations p, thelocus of possible locations 15 may instead be calculated as a curve (orsurface) which corresponds to a particular confidence level, forexample, between 50% and 95%. The curve (or surface) may be furtherconstrained to have a particular shape, for example an ellipse(ellipsoid or oblate/prolate spheroid). Referring in particular to FIG.8 , the ellipse 15 b (chained line) represents a locus of possiblepositions 15 in the form of a confidence boundary ellipse.

A still further alternative is to determine the locus of possiblelocations 15 as a curve (or surface) which encloses a threshold fractionof the alternative signal locations p, for example a fraction selectedto be between 0.5 and 0.99. The curve (or surface) may be furtherconstrained to have a particular shape, for example an ellipse(ellipsoid or oblate/prolate spheroid).

Referring again in particular to FIG. 3 , following generation of thelocus of possible positions 15 (step S6), it is determined whether thecalculated signal location r lies inside or outside the convex hull 5corresponding to the physical locations of the sensors (step S7).Alternatively, this determination (step S7) may be made based on whetherany (or all) of the calculated signal location r and all previouslycalculated signal locations r within a preceding time period T areinside or outside the convex hull 5.

If the calculated signal location r is within the convex hull 5 (stepS7|Yes) a first cluster filter is applied to the calculated signallocation r and previously calculated signal locations within a precedingtime period T (step S8). The first cluster filter applies circular (orspherical) boundaries having a predetermined radius w₀ (FIG. 10 ).

If the calculated signal location r is outside the convex hull (stepS7|No), a second cluster filter is applied to the calculated signallocation r and the previously calculated signal locations within thepreceding time period T (step S9). The second cluster filter applieselliptical (ellipsoidal or oblate/prolate spheroidal) boundaries havinga short axis equal to the radius w₀ used for the first cluster filter,and a ratio of long and short axes which is equal to a ratio of maximumand minimum distances spanning the locus of possible positions 15. Forexample, the ratio would be w₂/w₁ with w₁=w₀ using the rectangular locus15 a shown in FIG. 8 , or the ratio of semi-major and semi-minor axes ofthe elliptical locus 15 b shown in FIG. 8 . The long axis of eachelliptical (ellipsoidal or oblate/prolate spheroidal) boundary isaligned parallel to a direction which corresponds to the maximumdistance spanning the locus of possible positions 15.

The second cluster filter (step S9) may apply ellipsoidal boundaries inthe form of tri-axial ellipsoids, in which case there may be a further,intermediate axis in addition to the long and short axes. When thesecond cluster filter (step S9) applies ellipsoidal boundaries in theform of spheroids, the long axis or the short axis may correspond to apair of axes having equal length (oblate has a pair of equal long axesand prolate a pair of short axes).

The first and second cluster filters (steps S8, S9) preferably take theform of nearest neighbour cluster analysis methods. The first and secondcluster filters (steps S8, S9) may be applied in two or three dimensionsin dependence on whether the signal locations r are calculated in two orthree dimensions. For simplifying calculations, or when there is amixture of two and three dimensional calculated signal locations r, thesignal location r and/or any previously calculated signal locations rmay be projected onto a two-dimensional surface prior to application ofthe first and second cluster filters (steps S8, S9). Where the extent ofcoverage for the system 1 is relatively small, the two-dimensionalsurface for projections into two dimensions may take the form of aplane. However, when the extent of coverage of the system 1 is larger,such that the shape of the Earth cannot be neglected, thetwo-dimensional surface for projections into two dimensions may insteadtake the form of a portion of a spherical surface (e.g. corresponding toaltitude at the system 1 location, sea level etc). If the information isavailable for the location surrounding the system 1, the two-dimensionalsurface for projections into two dimensions may correspond to the ground(i.e. the surface of the earth/local topographic relief), in the form ofland, water or a combination.

Referring also to FIGS. 9 to 11 , the cluster filtering processes (stepsS8, S9) are further explained.

FIG. 9 shows a series of calculated locations r corresponding to apreceding time period.

Every time a signal location r is calculated, it is added to a buffer(or similar/equivalent data structure) which stores the present locationr and all the previously calculated locations r within a given timeperiod T Whilst signal locations r need not be calculated at regularintervals, this is expected to be common in practice. For the sake ofthe following explanation it will be assumed that the method of FIG. 3is carried out at intervals of duration α, where the time period Tcorresponds to a number H of intervals a, i.e. T=H·α. Let the locationcalculated corresponding to time t−h·α be denoted as r _(h) with h aninteger 0≤h≤H (such that r ₀ is the most recently calculated signallocation). The calculated signal location r and the previouslycalculated signal locations within the preceding time period T wouldthen correspond to the set {r ₀, r ₁, . . . , r _(h), . . . , r _(H)},for example stored in a buffer for use by the cluster filtering steps(S8 or S9) of the method.

In the example shown in FIG. 9 , H=15. The nature of time-of-arrivallocation finding calculations results in the influence of noise causingcalculated locations r ₁, . . . , r _(H) to become elongatedapproximately along a line 16 passing through the centroid of the set ofsensors 3 used for the calculation. Although there may be little or noelongation within the convex hull 5, the further outside the convex hull5 the true location of an emission source 2 is, the more pronounced thatelongation becomes.

The example shown in FIG. 9 corresponds schematically to a set {r ₀, r₁, . . . , r _(h), . . . , r _(H)} which might be observed for anemission source 2 lying outside the convex hull 5, for example emissionsource 2 b shown in FIG. 1 . The dashed ellipses shown in FIG. 9correspond to the loci 15 of possible positions calculated for thecorresponding signal locations r _(h), in the form of confidenceellipses. The loci of possible positions 15 may be aligned alongdifferent directions for each calculated location r _(h), for examplebecause subset of sensors 3 used (and hence the corresponding centroid)may be different between calculations of signal location r.

In order to provide the most accurate estimation of the location R of anemission source 2, it is desirable to combine multiple calculated signallocations belonging to the set {r ₀, r ₁, . . . , r _(h), . . . , r_(H)}. Cluster filtering is applied to screen out outliers (see forexample r ₄ and r ₉ in FIG. 9 ). Within the convex hull 5, thehereinbefore described elongation along the line 16 is relatively minor(or non-existent), and cluster filtering using circular (in 2D) orspherical (in 3D) bounding surfaces may be applied. However, outside theconvex hull, the inventors have observed from simulations and fieldtrials that the elongation of calculated signal locations resulting fromthe combination of noise with the nature of time-of-arrival locationfinding (multilateration) calculations, renders conventional clusterfiltering techniques less effective. Whilst the bounding surfaces forcluster filtering may be elongated to an ellipse (in 2D) or an ellipsoid(in 3D, for example a prolate spheroid), the question remains of whataspect ratio to use. The inventors have observed from simulations andfield trials that using a fixed aspect ratio is not appreciably moreeffective than simply using a larger circle (or sphere). Instead, theinventors have determined from simulations and field trials that theaccuracy of cluster filtering and locating an emission source 2 outsidethe convex hull 5 defined by the sensors may be improved based on theoutputs of generating a locus of possible positions 15 (step S6). Inparticular, the second cluster filter (step S9) uses bounding surfaceshaving an aspect ratio which is independently set for each location r_(h) of the set {r ₀, r ₁, . . . , r _(h), . . . , r _(H)} to be equalto the aspect ratio of the corresponding locus r _(h) of possiblepositions 15.

Referring in particular to FIG. 10 , a schematic example of applying thefirst cluster filter (step S8) is shown.

In FIG. 10 , the set {r ₀, r ₁, . . . , r _(h), . . . , r _(H)} of thecalculated signal location r ₀ and the previously calculated signallocations r ₁, . . . , r _(h), . . . , r _(H) within the preceding timeperiod T corresponds to H=8. The relative locations shown are schematicand intended for illustrative purposes. The dashed circles illustratecircular bounding curves 17 of fixed radius w₀, and for visual clarity abounding curve 17 is not drawn for the most recently calculated signallocation r ₀.

There are two main approaches to the cluster filtering. In the firstapproach, for a pair of locations r _(h), r _(k≠h) to be clustered thebounding circle 17 of one location r _(h), must encompass or intersectthe location of the other r _(k≠h) (the reverse is true by default forthe first cluster filter). In the second approach, the requirement isrelaxed such that a pair of locations r _(h), r _(k≠h) are clustered iftheir respective bounding circles 17 overlap. In either case, more thanone cluster may be determined, and locations r _(h), r _(k≠h) which arenot clustered when considered independently may become clustered viafurther locations.

Applying the first approach to the example shown in FIG. 10 , r ₀ iswithin the bounding circles 17 of each of r ₂, r ₅ and r ₆ so these forma cluster. Moreover, r ₃ is within the bounding circle 17 of r ₆, and sois added to the overall cluster which is the set {r ₀, r ₂, r ₃, r ₅, r₆}. Each of r ₁, r ₄ and r ₇ as illustrated is too far from any otherlocation r _(h) to form even a cluster of two.

In both the first and second cluster filters (steps S8, S9), clusters ofbetween two and a minimum threshold number of locations r _(h) (e.g. 5,10 etc) may be ignored.

Applying the second approach, locations r ₄ and r ₇ remain outside thecluster, since the respective bounding circles 17 do not intersect thoseof any other locations r _(h). However, the bounding circle 17 of thelocation r ₁ intersects at least the bounding circle 17 of the locationr ₅, so that r ₁ is also added to the cluster {r ₀, r ₁, r ₂, r ₃, r ₅,r ₆}.

Although in some examples clustering may be implemented such that acluster may only grow from the most recently calculated signal locationr ₀, it is preferred that clustering be permitted to grow from anylocation r _(h). This may result is determining two or more distinctclusters. If two or more clusters both exceed a minimum threshold numberof locations r _(h), this may be indicative of two distinct radioemission sources 2.

The radius w₀ used for a given system 1 will depend on a variety offactors including the specifics of the sensors 3, their physicallocations, the local electromagnetic noise environment, and type ofemission sources 2 it is desired to detect (e.g. drones, mobiletelephones etc), and so forth. The radius w₀ may be calibrated for aspecific installation using simulations and/or field trials withemission sources 2 having known locations. For example, if it is desiredto use a system 1 to detect drones, one or more types of drone includinglocation sensors (e.g. GPS, inertial compass etc) may be moved aroundthe area of the system 1 whilst the system logs calculated locations r_(h). The desired radius w₀ may then be calibrated based on a desiredmetric, for example, so that a false positive rate of detections doesnot exceed a maximum tolerance. The minimum threshold number oflocations r _(h) for a cluster to be counted may also be a variable insuch a calibration.

Referring in particular to FIG. 11 , a schematic example of applying thesecond cluster filter (step S9) is shown.

In FIG. 11 , the set {r ₀, r ₁, . . . , r _(h), . . . , r _(H)} of thecalculated signal location r ₀ and the previously calculated signallocations r ₁, . . . , r _(h), . . . , r _(H) within the preceding timeperiod T corresponds to H=7. The relative locations shown are schematicand intended for illustrative purposes. The dashed ellipses illustrateelliptical bounding curves 18. Unlike for the bounding circles 17 of thefirst cluster filter (step S8), each elliptical bounding curve 18 isdifferent in dependence on the locus of possible positions 15 generatedfor the corresponding calculated signal location r _(h). Each ellipticalbounding curve 18 has a fixed semi-minor axis of length w₀, equal to theradii w₀ of the bounding circles 17 of the first cluster filter. Eachelliptical bounding curve 18 has a semi-major axis aligned parallel to adirection corresponding to the maximum dimension of the locus ofpossible locations 15 for the corresponding calculated signal location r_(h). For example, if the locus 15 is a rectangle 15 a, the semi-majoraxis of the elliptical bounding curve 18 will be parallel to the longside w₂, whereas if the locus 15 is an ellipse 15 b, the semi-major axisof the elliptical bounding curve 18 will be parallel to the semi-majoraxis of the elliptical locus 15 b. However, the length of the semi-majoraxis of each elliptical bounding curve 18 is set so that the ellipticalbounding curve 18 will have an aspect ratio (of semi-major to semi-minoraxes) which is equal to a ratio of maximum and minimum distancesspanning the locus of possible locations 15 of the correspondingcalculated signal location r _(h).

For the 3D case, the long axis (or axes) of a spheroid are similarlyaligned with the respective axis (or axes) of the locus of possiblepositions. If bounding surfaces take the form of tri-axial ellipsoids,then the ratio of the intermediate axis to the short axis may also beset based on a dimension of the locus 15 along a correspondingdirection.

The first and second approaches to clustering (as explained in relationto the first cluster filter (step S8)) also apply to the second clusterfilter (step S9). It is preferable that both first and second clusterfilters (steps S8, S9) apply the same approach. It should be noted thatfor the second cluster filter (step S9) applying the first approach, ifthe bounding ellipse 18 of one location r _(h) encompasses or intersectsthe location of the other r _(k≠h), the reverse is not necessarily true,but a single overlap may be sufficient for clustering. Alternatively, athird approach may apply condition of mutual overlap for clustering,i.e. such that clustering is found only if the bounding ellipse 18 oflocation r _(h) encompasses or intersects the location r _(k≠h) whilstthe bounding ellipse 18 of location r _(k≠h) also encompasses orintersects the location r _(h).

Applying the first approach to the example shown in FIG. 11 , a firstcluster is formed by the set {r ₀, r ₁, r ₃, r ₅} and a second clusteris formed by the set {r ₂, r ₆}. It may be noted that although thebounding ellipse 18 corresponding to location r ₃ does not encompass thelocation r ₀, the location is added to the first cluster {r ₀, r ₁, r ₃,r ₅} because the bounding ellipse 18 corresponding to location r ₀ doesencompass the location r ₃. Depending on whether a minimum numberthreshold is applied, the second cluster {r ₂, r ₆} may be ignored. Forexample, if the minimum threshold was three, the first cluster would beused and the second ignored, and so forth.

Applying the second approach to the example shown in FIG. 11 , the firstcluster would additionally include the location r ₄, since thecorresponding bounding ellipse 18 intersects the bounding ellipses 18 oflocations r ₁ and r ₅ (though only one is needed)

Simulations and/or experiments described hereinbefore in relation tocalibrating the radius w₀ should preferably also includesimulations/measurements where the emission source 2 having knownlocation is both inside and outside the convex hull 5, in order tocalibrate the performance of both first and second cluster filters(steps S8, S9) for a particular system 1, its environment, and intendedapplication.

Referring again in particular to FIG. 3 , one or more radio emissionsource location estimates R are calculated (step S10). Each radioemission source location estimate R corresponds to a different clusterdetermined by the first or second cluster filter (steps S8, S9).Consequently, there may be zero, one, two or more radio emission sourcelocation estimates R. For example, if a minimum threshold number oflocations r _(h) is applied to screen out small clusters, then there maybe no clusters which meet or exceed that minimum threshold.

Each estimated radio emission source location R may be simply determinedas an average of a respective cluster of signal locations r _(h). Forexample, a first estimated radio emission source location R ₁ may be theaverage of a first cluster of signal locations r _(h) and a secondestimated radio emission source location R ₂ may be the average of asecond cluster of signal locations r _(h). The average may be of anysuitable type, for example a mean or a weighted mean. For example, eachsignal location r _(h) in a cluster may be weighted inversely to an areaor volume of the respective locus of possible locations 15 (so thatthose less sensitive to noise are weighted preferentially). However, amean average need not be used, and other measures such as a median maybe used (e.g. combining a median latitude with a median longitude).

An averaging process does not take into account that an emission source2 may well be moving during the course of the preceding period T usedfor clustering. An alternative approach to calculation of the estimatedradio emission source location R for each cluster is to fit a linearregression line to the respective cluster of signal locations r _(h)(using time as the independent variable), followed by extrapolating thecalculated linear regression line to the output time t.

In other implementations, a hybrid of the two approaches (average andregression line) may be applied. For example, each cluster of signallocations r _(h) may be tracked across the preceding time period T Thismay also help to further screen out any errors, since a cluster which isnot persistent may be erroneous. When a given cluster is stationary, thecorresponding estimated radio emission source location R is calculatedas an average. However, when a given cluster is moving, thecorresponding estimated radio emission source location R is calculatedusing the regression line approach.

Each new, distinct cluster which is tracked may be initialised asstationary. A new cluster may be one having no corresponding clusterduring an immediately previous iteration of the method. A minimumpersistence may be imposed before outputting an estimated radio emissionsource location R corresponding to each new cluster. A stationarycluster may be changed to a moving cluster in response to a speed of therespective estimated radio emission source location R exceeding a motionthreshold. Similarly, a moving cluster may be changed to a stationarycluster in response to a speed of the respective estimated radioemission source location R being below a static threshold. The motionthreshold may be equal to the static threshold, but does not need to beequal. In some implementations, it may be possible for determination ofwhether a cluster is moving or stationary may be based at least on parton Doppler frequency shifts between the radio signal datasets. Thislatter option will only be viable for some combinations of sensor 3frequency resolution of emission source 2 velocities. In particular,sole reliance on Doppler may be inadvisable as it may miss movingemissions sources 2 moving tangentially to a radial line originatingat/within the system 1.

In some examples, every signal location r _(n) belonging to a cluster ofsignal locations r _(n) may be output as a separate estimated radioemission source location R. This may be in addition to an averaged orextrapolated location R, or instead of an averaged or extrapolatedlocation R.

Once calculated, the one or more estimated radio emission sourcelocations R are output (step S11). For example, when the system 1 isoperating live (in real time), the one or more estimated radio emissionsource locations R may be output to a display being monitored by a user,via a message sent to a user over a network connection, via SMS, viae-mail, by causing a speaker to output an alarm signal, and so forth.When the system 1 is not operating live, the one or more estimated radioemission source locations R may be stored to a computer readable medium(e.g. a log file) for subsequent analyses.

In addition to alerting a user, one or more hardware systems connectedto the system 1 may also be activated and/or controlled in dependence onoutputting at least one estimated radio emission source location R. Forexample, in the application of detecting drones trespassing inrestricted airspace, the output step S11 may include causing one or moreoptical telescopes (not shown) and/or hardware drone countermeasures(not shown) to be directed towards one or more estimated radio emissionsource locations R. This may help to improve reaction times forpositively verifying that a drone is trespassing and removing apotential threat. Drone countermeasures may include one or more knownsystems including without limitation lasers, radio frequency jammers,global positioning system (GPS) spoofers, high power microwave devices,net launchers, interception drones and so forth.

The method has been explained as including the generation of the locusof possible positions 15 (step S6) unconditionally, since the locus 15provides a useful measure of sensitivity of any given calculatedlocation r _(h) is useful information in and of itself. However, sincethis information is only required for the second cluster filter (stepS9), in some implementations computational overheads may be reduced byinstead making the generation of the locus of possible positions 15(step S6) conditional on the calculated signal location r (r ₀) beingoutside the convex hull 5 (step S7|No).

Bearing Calculation

In addition to, or instead of, calculating zero of more estimates ofradio emission source location R, the apparatus 6 may calculate abearing angle θ_(B) to an emission source 2.

For example, the calculation of a bearing angle θ_(B) may be included atany point in the method of FIG. 3 (see step S13) following thecalculation of the signal location r (step S5). In an alternative method(not specifically illustrated but including steps S1 through S5) thecalculation of a bearing angle θ_(B) may be performed in isolation ofcalculating estimates of radio emission source location(s) R.

Referring also to FIGS. 12 and 13 , calculation of a bearing angle θ_(B)is further described.

The calculation is performed based on the signal location r and thepreviously calculated signal locations r within the preceding timeperiod T, i.e. the same set of signal locations r used for the clusteranalyses (step S8 or S9). For the sake of explanations, again assumethat the signal locations r are calculated at regular intervals so thatthis set is {r ₀, r ₁, . . . , r _(h), . . . , r _(H)} as definedhereinbefore.

The bearing angle θ_(B) is determined as the angle θ which maximises anumber of signal locations r _(h) within an angular threshold δθ of thebearing angle θ_(B).

Referring in particular to FIG. 12 , a number H=18 of signal locations rare illustrated, with dashed lines delineating fixed angular intervalsof δθ=10⁰ fanning out from an origin point 19.

Referring in particular to FIG. 13 , determining the bearing angle θ_(B)may include calculating an angular histogram having a bin width equal tothe angular threshold δθ. The bearing angle θ_(B) is then determined asa central angle of an angular histogram bin containing the greatestnumber of signal locations r _(h). In the example shown in FIGS. 12 and13 , the bearing angle is θ_(B)=25° from the y-axis as illustrated,because the angular bin between 20° and 30° contains the largest number(eight) of signal locations r _(h). A minimum threshold may be appliedto the number of locations r _(h) within the angular bin before a peakis counted and used for determining a corresponding bearing angle θ_(B).If there are additional, distinct peaks in the angular histogram, eachdistinct peak may be assigned a corresponding bearing angle θ_(B). Thismay occur when there are two or more emission sources 2 active at thesame time.

Alternatively, the angle θ may be swept through an arc (up to 2π/360°)in angular increments smaller than a desired angular threshold δθ (e.g.increments of δθ/10), counting the number of signal locations r _(h) inthe range θ-δθ<θ≤θ+δθ, until one or more peaks is found and assigned asthe bearing angle θ. As with the angular histogram method, a minimumthreshold number of locations r _(h) may be imposed, and in some casesmultiple peaks may exceed the minimum threshold and consequently betreated as separate bearing angles θ_(B).

If calculated, one or more bearing angles θ_(B) may be output along with(or instead of) the estimated radio emission source location(s) R (stepS11), in any way described hereinbefore.

Calculation of a bearing angle θ_(B) requires definition of an originpoint 19. In general, the bearing angle θ_(B) may be calculated tooriginate from an origin point 19 corresponding to a centroid of thesensors 3 of the system 1.

Alternatively, the origin point 19 may be set and/or modified tocorrespond to a user defined location within the convex hull 5. In someimplementations, the bearing angles θ_(B) may be calculated from anorigin point 19 corresponding to one of the sensors 3, or to an opticaltelescope (not shown) or a hardware drone countermeasure (not shown).Such an optical telescope and/or drone countermeasure may also be causedto point along the output bearing angle θ_(B).

An extension of the method of calculating a bearing angle θ_(B) may beimplemented. In a first step, a peak in an angular histogram or angularsweep may be determined as hereinbefore described from an arbitraryorigin (e.g. the centroid of the sensors 3). In a second step, a linearregression may be applied to the locations r _(h) corresponding to that(or each) peak, to obtain a unique bearing angle. An origination pointmay be set to any position along the unique bearing angle, and may bechosen for convenience in a given application. For example, the locationof an optical telescope or drone countermeasure when the system is usedto detect unauthorised drones in an airspace.

Outer Perimeter

The determination of one or more bearing angles θ_(B) (step S13) maymaintain reliability out to a larger distance from the convex hull 5than the determination of one or more estimated radio emission sourcelocations R (steps S6 through S10).

Consequently, in an implementation of the method of FIG. 3 whichincludes the optional step of determining a bearing angle θ_(B) (stepS13), computational overheads may be reduced and false positives alsoreduced by omitting the determination of estimated radio emission sourcelocation(s) R (steps S6 through S10) if the calculated signal location ris far enough from the system 1.

For example, the method may bypass the determination of one or moreestimated radio emission source location R (steps S6 through S10) inresponse to the calculated signal location r is outside an outerperimeter 20 (FIG. 14 ) (step S14|No). The outer perimeter 20 is anouter perimeter in the sense that for a signal location r calculated asbeing outside this perimeter 20, estimates of radio emission sourcelocation(s) R are not made.

Referring also to FIG. 14 , an outer perimeter 20 is illustratedcorresponding to an exemplary system 1 including four sensors 3.

The outer perimeter 20 corresponds to the locus of positions on whichthe sensors 3 (and corresponding convex hull 5) subtend a constant angleβ. In the example shown in FIG. 14 , the illustrated outer perimeter 20corresponds to an angle of β=30°, which in simulations and field trialsthe inventors have found to be a suitable angle in many realapplications. Three points P₁, P₂, P₃ are highlighted around the outerperimeter 20 to illustrate that the angle β subtended by the sensors 3from any point is fixed.

Referring also to FIG. 15 , another outer perimeter 20 is illustratedcorresponding to an exemplary system 1 including eight sensors 3,corresponding to a fixed angle of β=30°.

Although the examples of outer perimeter 20 correspond to the same fixedangle of β=30°, the angle β may be varied. For example, for a system 1intended to detect objects such as drones in and around a protectedairspace, the outer perimeter 20 used may correspond to an angle β ofbetween (and including) 20° and 40°. Equally, alternative methods ofdefining the outer perimeter 20 may be used, for example, the outerperimeter 20 may be defined as a locus of points a fixed distance fromthe convex hull 5.

In this way, bearing angles θ_(B) may be determined regardless of range(step S13), the first cluster filter may be applied (step S8) inresponse to the calculated signal location r is within the convex hull 5(step S7|Yes) and within the outer perimeter 20 (step S14|Yes) (thelatter condition being always true if the former is true), and thesecond cluster filter may be applied (step S9) in response to thecalculated signal location r is outside the convex hull 5 (step S7|No)and within the outer perimeter 20 (step S14|Yes).

In this way, a bearing angle may θ_(B) always be calculated to provideat least a direction to a radio emission source 2. As the radio emissionsource 2 moves closer and crosses the outer perimeter 20, clusterfiltering to calculate and output an estimated radio emission sourcelocation R will be carried out using the second cluster filter (stepS9), in addition to the bearing angle θ_(B). As the radio emissionsource 2 moves closer still and crosses inside the convex hull 5, thefirst cluster filter (step S8) will be applied to determining anestimated radio emission source location R Although it is possible tocontinue calculating the bearing angle θ_(B) for radio emission sources2 within the convex hull 5, the availability of a more accurateestimated radio emission source location R at such ranges means thatcalculation of the bearing angle θ_(B) may be of reduced value, and maybe omitted for radio emission sources 2 within the convex hull 5.

MODIFICATIONS

It will be appreciated that various modifications may be made to theembodiments hereinbefore described. Such modifications may involveequivalent and other features which are already known in the design anduse of methods and apparatuses for radio location finding based ontimes-of-arrival, and which may be used instead of or in addition tofeatures already described herein. Features of one embodiment may bereplaced or supplemented by features of another embodiment.

Although three particular approaches to cluster filtering have beendescribed, any approaches using the same bounding curves (or surfaces)may be used.

Although claims have been formulated in this application to particularcombinations of features, it should be understood that the scope of thedisclosure of the present invention also includes any novel features orany novel combination of features disclosed herein either explicitly orimplicitly or any generalization thereof, whether or not it relates tothe same invention as presently claimed in any claim and whether or notit mitigates any or all of the same technical problems as does thepresent invention. The applicants hereby give notice that new claims maybe formulated to such features and/or combinations of such featuresduring the prosecution of the present application or of any furtherapplication derived therefrom.

1. A method of passively locating a radio emission source, comprising:receiving radio signal datasets corresponding to each of three of moresensors, each sensor comprising at least one radio receiver; receivingor retrieving a physical location corresponding to each sensor, whereinthe physical locations define a convex hull; determining whether anemitter signal within a target frequency range is present in any of theradio signal datasets, and assigning any radio signal dataset whichcomprises the emitter signal as a detection dataset; in response todetermining three or more detection datasets: calculating a signallocation based on arrival times of the emitter signal and the respectivephysical locations; generating a locus of possible positions based oncalculating two or more alternative signal locations, each alternativesignal location calculated by adding synthetic noise to one or more ofthe detection datasets and repeating the calculations used to calculatethe signal location; in response to the signal location is within theconvex hull, applying a first cluster filter to the signal location andpreviously calculated signal locations within a preceding time period,wherein the first cluster filter applies circular or sphericalboundaries having a fixed radius for each of the signal location and thepreviously calculated signal locations; in response to the signallocation is outside the convex hull, applying a second cluster filter tothe signal location and the previously calculated signal locationswithin the preceding time period, wherein the second cluster filterapplies elliptical or ellipsoidal boundaries for each of the signallocation and the previously calculated signal locations, each ellipticalor ellipsoidal boundary having a long axis and a short axis with lengthequal to the fixed radius, wherein a ratio of the long and short axes isequal to a ratio of maximum and minimum distances spanning therespective locus of possible locations, and the long axis is alignedparallel to the maximum distance; outputting one or more estimated radioemission source locations, each estimated radio emission source locationdetermined based on a respective cluster of signal locations.
 2. Amethod according to claim 1, wherein determining whether the emittersignal within the target frequency range is present in any of radiosignal datasets comprises performing a correlation analysis of eachradio signal dataset against each other radio signal dataset.
 3. Themethod according to claim 1, wherein in response to determining threedetection datasets, the signal location is calculated in two dimensions;or wherein in response to determining four or more detection datasets,the signal location is calculated in three dimensions.
 4. (canceled) 5.The method according to claim 1, wherein the signal location is onlycalculated in response to determining four or more detection datasets,and wherein the signal location is calculated in three dimensions. 6.The method according to claim 1, wherein the signal location and/or anypreviously calculated signal locations are projected onto atwo-dimensional surface prior to application of the first and secondcluster filters.
 7. The method according to claim 1, wherein addingsynthetic noise to a detection dataset comprises applying a temporaloffset to that detection dataset.
 8. The method according to claim 1,wherein adding synthetic noise to a detection dataset comprisesgenerating a noise signal and adding it to that detection dataset. 9.The method according to claim 1, wherein the locus of possible positionsis generated by fitting a curve or surface to the alternative locations.10. The method according to claim 1, wherein an estimated radio emissionsource location is determined and output to correspond to each clusterof signal locations which includes more than a threshold number.
 11. Themethod according to claim 1, wherein each estimated radio emissionsource location is determined as an average of a respective cluster ofsignal locations.
 12. The method according to claim 1, wherein eachestimated radio emission source location is determined by: fitting alinear regression line to the respective cluster of signal locations;extrapolating the linear regression line to the output time.
 13. Themethod according to claim 1, further comprising tracking clusters ofsignal locations across the preceding time period, wherein: in responseto a cluster is stationary, determining the estimated radio emissionsource location as an average of the signal locations belonging to thatcluster; in response to a cluster is moving, fitting a linear regressionline to the signal locations belonging to that cluster and extrapolatingthe linear regression line to an output time.
 14. The method accordingto claim 1, further comprising causing one or more optical telescopesand/or hardware drone countermeasures to be directed towards acorresponding estimated radio emission source location.
 15. The methodaccording to claim 1, further comprising: based on the signal locationand the previously calculated signal locations within the preceding timeperiod, determining a bearing angle which maximises a number of signallocations within an angular threshold of the bearing angle.
 16. A methodaccording to claim 15, further comprising receiving or calculating anouter perimeter such that, when viewed from any position on the outerperimeter, the convex hull subtends a fixed angle; wherein applicationof the first cluster filter is further conditional upon the signallocation is within the outer perimeter; wherein application of thesecond cluster filter is further conditional upon the signal location iswithin the outer perimeter.
 17. A method of calculating a bearing to aradio emission source, comprising: receiving radio signal datasetscorresponding to each of three of more sensors, each sensor comprisingat least one radio receiver; receiving or retrieving a physical locationcorresponding to each sensor, wherein the physical locations define aconvex hull; determining whether an emitter signal within a targetfrequency range is present in any of the radio signal datasets, andassigning any radio signal dataset which comprises the emitter signal asa detection dataset; in response to determining three or more detectiondatasets: calculating a signal location based on arrival times of theemitter signal and the respective physical locations; based on thesignal location and previously calculated signal locations within apreceding time period, determining a bearing angle which maximises anumber of signal locations within an angular threshold of the bearingangle; outputting the bearing angle.
 18. Apparatus for passivelylocating a radio emission source, comprising: a communications interfaceconfigured to receive radio signal datasets corresponding to each ofthree of more sensors, each sensor comprising at least one radioreceiver; wherein the communications interface is further configured toreceive a physical location corresponding to each sensor, or theapparatus stores the physical locations and is configured to retrievethe physical locations, wherein the physical locations define a convexhull; the apparatus configured: to receive radio signal datasetscorresponding to each of three of more sensors, each sensor comprisingat least one radio receiver; to receive or retrieve a physical locationcorresponding to each sensor, wherein the physical locations define aconvex hull; to determine whether an emitter signal within a targetfrequency range is present in any of the radio signal datasets, and toassign any radio signal dataset which comprises the emitter signal as adetection dataset; in response to determining three or more detectiondatasets: to calculate a signal location based on arrival times of theemitter signal and the respective physical locations; to generate alocus of possible positions based on calculating two or more alternativesignal locations, each alternative signal location calculated by addingsynthetic noise to one or more of the detection datasets and repeatingthe calculations used to calculate the signal location; in response tothe signal location is within the convex hull, to apply a first clusterfilter to the signal location and previously calculated signal locationswithin a preceding time period, wherein the first cluster filter appliescircular or spherical boundaries having a fixed radius for each of thesignal location and the previously calculated signal locations; inresponse to the signal location is outside the convex hull, to apply asecond cluster filter to the signal location and the previouslycalculated signal locations within the preceding time period, whereinthe second cluster filter applies elliptical or ellipsoidal boundariesfor each of the signal location and the previously calculated signallocations, each elliptical or ellipsoidal boundary having a long axisand a short axis with length equal to the fixed radius, wherein a ratioof the long and short axes is equal to a ratio of maximum and minimumdistances spanning the respective locus of possible locations, and thelong axis is aligned parallel to the maximum distance; to output one ormore estimated radio emission source locations, each estimated radioemission source location determined based on a respective cluster ofsignal locations.
 19. A system comprising: three or more sensors, eachsensor comprising at least one radio receiver, wherein physicallocations of the sensors define a convex hull; the apparatus accordingto claim 18, configured to receive respective radio signal datasets fromthe three or more sensors.
 20. The system according to claim 19, whereineach sensor is configured to transmit the corresponding radio signaldataset continuously.
 21. The system according to claim 19, wherein eachsensor is configured to locally cache the respective radio signaldataset and to transmit the cached radio signal dataset to the apparatusin batches.